創建一個for回圈或函式以從資料框創建多個熱圖

我對 python 比較陌生，所以我對 for/while 回圈或函式不是很好。

基本上，我有一個如下所示的資料框：

temp | dewpoint | wind | precip_rate_hr | total_snow
-------------------------------------------------
31        20       3         0.2            2.1
29        25       12         0.01           0.7
30        30       17         0.5            4.1
...      ...      ...         ...            ...

我一直在使用 seaborne 創建熱圖，比較前四列，顯示兩個變數之間的 total_snow 平均值。抱歉，如果這聽起來不對。這是我的代碼：

snow_data_percentile_10_temp = np.percentile(snow_data['temp'], 10)
snow_data_percentile_50_temp = np.percentile(snow_data['temp'], 50)
snow_data_percentile_75_temp = np.percentile(snow_data['temp'], 75)

snow_data_percentile_10_dewpt = np.percentile(snow_data['dewpoint'], 10)
snow_data_percentile_50_dewpt = np.percentile(snow_data['dewpoint'], 50)
snow_data_percentile_75_dewpt = np.percentile(snow_data['dewpoint'], 75)

snow_data['temp_bin'] = pd.cut(snow_data['temp'], [0, 10.4, 23.5, 28.75, 37], labels=['<10.4', '10.4-23.5', '23.5-28.75', '>28.75'])
snow_data['dewpt_bin'] = pd.cut(snow_data['dewpoint'], [0, 4.1, 15, 19.75, 33], labels=['<4.1', '4.1-15', '15-19.75', '>19.75'])

avg_snow = snow_data.groupby(['temp_bin','dewpt_bin'], as_index=False)['total_snow'].mean()

data_fp = avg_snow.pivot_table(index='temp_bin', columns='dewpt_bin', values='total_snow')
sns.set(font_scale=1.2)
f, ax = plt.subplots(figsize=(25,25))
sns.set(font_scale=2.0)
sns.heatmap(data_fp, annot=True, fmt='g', linewidth=0.5) 
ax.set_title('Average Snow Total on Days that Met Specific Temperature and Dewpoint Criteria', fontsize=20)

Here's a capture of the heatmap. The values displayed on the bins are the average snow totals for those bins. Is there a way to streamline this code? I need to create heatmaps for temp vs wind, temp vs precip_rate_hr, dewpoint vs wind, dewpoint vs precip_rate_hr, wind vs precip_rate_hr. I also have another, larger dataset I'll need to work through. Right now, I'm just copying and pasting the code into new files and changing it around some to get the rest of the heatmaps. That doesn't take too long, but I would like to automate it more and prevent myself from having numerous code files. Any help would be appreciated!

uj5u.com熱心網友回復：

這是在回圈中創建所有圖表的一種方法。它不是最干凈的，因為會有重復的圖表（但交換了軸）。

我的資料：

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

snow_data = pd.DataFrame(data={"temp": np.random.randint(20, 40, 50), "dewpoint": np.random.randint(15, 40, 50),
                               "wind": np.random.randint(0, 20, 50), "precip_rate_hr": np.random.random(50),
                               "total_snow": np.random.random(50)*10})

創建類別（這不是回圈，因為箱都是不同的）：

snow_data['temp_bin'] = pd.cut(snow_data['temp'], [0, 10.4, 23.5, 28.75, 37], labels=['<10.4', '10.4-23.5', '23.5-28.75', '>28.75'])
snow_data['dewpt_bin'] = pd.cut(snow_data['dewpoint'], [0, 4.1, 15, 19.75, 33], labels=['<4.1', '4.1-15', '15-19.75', '>19.75'])
snow_data['wind_bin'] = pd.cut(snow_data['wind'], [0, 5, 10, 15, 20], labels=['<5', '5-10', '10-15', '15-20'])
snow_data['precip_rate_hr_bin'] = pd.cut(snow_data['precip_rate_hr'], [0, 0.25, 0.5, 0.75, 1], labels=['<0.25', '0.25-0.5', '0.5-0.75', '>0.75'])

回圈：

# List of all _bin columns to loop through
bin_cols = ['temp_bin', 'dewpt_bin', 'wind_bin', 'precip_rate_hr_bin']

# First factor
for i in bin_cols:
    # Second factor
    for j in bin_cols:
        # Need to ensure you aren't grouping the data by the same column twice!
        if j != i:
            # Average now mean for bin groups
            avg_snow = snow_data.groupby([i, j], as_index=False)['total_snow'].mean()
            # Title for plot
            title = 'Average Snow Total on Days that Met Specific '   i[: -4]   ' and '   j[: -4]   ' Criteria'
            # Pivot table
            data_fp = avg_snow.pivot_table(index=i, columns=j, values='total_snow')
            # Plot
            sns.set(font_scale=1.2)
            f, ax = plt.subplots(figsize=(25, 25))
            sns.set(font_scale=2.0)
            sns.heatmap(data_fp, annot=True, fmt='g', linewidth=0.5)
            ax.set_title(title, fontsize=20)
            plt.show()

我同意 Parfait 關于不需要 的評論np.percentile，除非您使用這些來為 _bin 列找到合適的類別。

標籤：python pandas function for-loop seaborn

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